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Open AccessArticle

The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory

School of Forest Resources, University of Maine, 5755 Nutting Hall, Orono, ME 04469-5755, USA
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Remote Sens. 2018, 10(4), 649; https://doi.org/10.3390/rs10040649
Received: 6 February 2018 / Revised: 6 April 2018 / Accepted: 13 April 2018 / Published: 23 April 2018
(This article belongs to the Section Forest Remote Sensing)
As light detection and ranging (LiDAR) technology becomes more available, it has become common to use these datasets to generate remotely sensed forest inventories across landscapes. Traditional methods for generating these inventories employ the use of height and proportion metrics to measure LiDAR returns and relate these back to field data using predictive models. Here, we employ a three-dimensional convolutional neural network (CNN), a deep learning technique that scans the LiDAR data and automatically generates useful features for predicting forest attributes. We test the accuracy in estimating forest attributes using the three-dimensional implementations of different CNN models commonly used in the field of image recognition. Using the best performing model architecture, we compared CNN performance to models developed using traditional height metrics. The results of this comparison show that CNNs produced 12% less prediction error when estimating biomass, 6% less in estimating tree count, and 2% less when estimating the percentage of needleleaf trees. We conclude that using CNNs can be a more accurate means of interpreting LiDAR data for forest inventories compared to standard approaches. View Full-Text
Keywords: deep learning; artificial neural network; machine learning; ALS; LiDAR; enhanced forest inventory; area-based deep learning; artificial neural network; machine learning; ALS; LiDAR; enhanced forest inventory; area-based
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Ayrey, E.; Hayes, D.J. The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory. Remote Sens. 2018, 10, 649.

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